IEEE Access (Jan 2021)

Joint Beamforming and Power Allocation for Multiuser MISO Broadcast Channel SWIPT Employing OFDM

  • Richard P. Ketcham,
  • Jeroen Verdyck,
  • Marc Moonen

DOI
https://doi.org/10.1109/ACCESS.2021.3132769
Journal volume & issue
Vol. 9
pp. 165154 – 165172

Abstract

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Resource allocation strategies for simultaneous wireless information and power transfer (SWIPT) employing orthogonal frequency division multiplexing (OFDM) typically rely on subcarrier assignment for multiple access. For information transfer, however, subcarrier assignment is only sum-rate optimal when the network is interference limited; otherwise, it is optimal to reuse subcarriers. Beamforming-based space-division multiple access (SDMA) is a multiple access technique that enables subcarrier reuse by managing interference. Although beamforming is considered a key SWIPT component, OFDM-based SWIPT has yet to utilize beamforming for SDMA. As such, this paper seeks to develop a low-complexity resource allocation strategy to jointly allocate power and design beamforming vectors for an OFDM-based multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) SWIPT system capable of utilizing SDMA. To this end, a non-convex optimization problem is formulated in which a weighted sum of user data rates is maximized subject to meeting receive and transmit power constraints. The time-sharing property of multicarrier systems is observed to justify utilizing Lagrangian decomposition despite the non-convexity. Whereas previous SWIPT research used Lagrangian decomposition to simplify subcarrier assignment, this paper instead uses it to enable SDMA through beamforming. Beamforming is achieved via a medium access channel (MAC)-BC duality, which enables a closed-form solution for the beamforming vectors given the dual MAC power allocation. The dual MAC power allocation is approximated using a successive convex approximation (SCA) technique. The result is a resource allocation algorithm that provides close-to-optimal performance with low computational complexity.

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